Simulation Experiments Research Articles

Rapid and quantitative CEST-MRI sequence using water presaturation.

Despite the significant potential for in vivo metabolic imaging in preclinical and clinical applications, CEST MRI suffers from long scan time and inaccurate quantification. This study aims to suppress the contaminations among signals under different frequencies, which could shorten the TR and thereby facilitate CEST imaging acceleration and quantification. A novel sequence is proposed by applying a water-presaturation (WPS) module at the beginning of each TR. WPS CEST quickly knocks down the residual signal from previous TRs so that the magnetization of all TRs recovers from zero, which aligns well with the formula of quasi-steady-state theorem and enables accurate quantification within shorter TR. WPS CEST was assessed by simulations, creatine phantom, and healthy human brain scans at 3 T. In simulation and phantom experiment, WPS CEST allows accurate estimation of exchange rate (ksw) using omega plot and using shorter delay time (Td) and saturation time (Ts) (e.g., 1 s/1 s) compared with the conventional CEST. Simulations further showed that WPS CEST could obtain consistent spin-lock relaxation (R1ρ) values over varied Tds and Tss. Six human scans indicated that R1ρ collected from conventional sequences showed significant differences between two groups with Td and Ts of (1 s/1 s) and (2 s/2 s) (amide: 1.721 ± 0.051 s-1 vs. 1.622 ± 0.050 s-1, p = 0.001; nuclear Overhauser enhancement: 1.792 ± 0.046 s-1 vs. 1.687 ± 0.053 s-1, p = 0.004), whereas WPS CEST scans using these 2 Td/Ts values obtained the same mean R1ρ (amide: 1.616 ± 0.053 s-1 vs. 1.616 ± 0.048 s-1, p = 0.862; nuclear Overhauser enhancement: 1.688 ± 0.064 s-1 vs. 1.684 ± 0.054 s-1, p = 0.544). WPS CEST demonstrated accurate quantitation within shorter TR compared with conventional sequences, and thereby may allow rapid quantitative CEST scans in various situations.

Optimal iterative learning PI controller for SISO and MIMO processes with machine learning validation for performance prediction

The multivariable process plays a significant role in industrial applications, and designing a controller for the Multi-Input Multi-Output process is challenging due to dynamic process changes and interactions between system variables. Traditionally, the PI family of controllers has been used for its simple design, easy tuning, and quick deployment. However, these processes require complex control actions due to multiple loops in process plants. Thus, this paper proposes an Iterative Learning Controller Dead-time compensating PI, which utilizes the newly developed hybrid Simulated Annealing-Ant Lion Optimization algorithm for Single-Input Single-Output process simulation and real-time experimentation on the Quadruple Tank System. To validate the effectiveness of the developed controller, Machine Learning techniques such as regression and ensemble trees are used to accurately predict the actual system response using error values from respective processes. The simulation and experimental results demonstrate that the proposed controller achieved better performance. The regression and ensemble tree algorithm models effectively predicted the actual response. The obtained data shows that the proposed controller improved system stability and robustness by minimizing nearly half of the overshoot and improving settling time, with an average of 29.9596% faster than the other controller in the SISO process and 14.6116% in the MIMO process.

A comprehensive propulsion system analysis: an integrated approach utilising engine simulation and free-running model experiments

Regulations concerning the environmental impact and energy efficiency of ships are becoming increasingly stringent. To meet these demanding criteria, new technologies are actively being developed to achieve emission reductions while maintaining efficient fuel energy conversion into propulsion power. However, the propulsion system behaviour in a dynamic environment of the actual sea is highly nonlinear and complex, and a profound understanding of the mutual interactions is still lacking. This paper proposes an integrated approach for comprehensive propulsion system analysis. The key feature of this technique is the utilisation of an intelligent propeller drive controlled by a marine diesel engine simulator (MDES) to drive the free-running model of a ship, providing real-like behaviour of the engine model in real-time in response to actual measured values. The core of the MDES consists of a continuous cycle-mean value dynamic engine model supplemented with a detailed combustion cycle evaluation. The developed fast calculation algorithm of crank-angle resolved thermodynamic equations of the gas state in the engine cylinder enables the complete engine model to be integrated into the real-time environment of a physical model ship. Thus, the propulsion system's performance can be analysed under conditions that closely mimic those of the actual sea environment.

Capacity analysis of short-distance continuous diversion areas on mountainous urban expressways.

The short-distance continuous diversion area plays a crucial role within mountainous urban expressway systems, significantly enhancing the efficiency of specialized road sections through capacity analysis. This study develops a capacity calculation model tailored to the diversion area's unique characteristics and principal capacity-influencing factors. Initially, the research focuses on a specific short-distance continuous diversion area of a mountainous urban expressway, employing video trajectory tracking technology to gather trajectory data. This data serves as the basis for analyzing road and traffic characteristics. Subsequently, the model computes the capacity influenced by eight variables, including diversion point spacing and deceleration lane length, using VISSIM simulation experiments. A gray correlation analysis identifies key factors, which guide the establishment of the model's fundamental structure through two-factor surface fitting results. Mathematical statistical methods are then applied to resolve the model's parameters, culminating in a robust capacity calculation model. The findings reveal that diversion point spacing, along with primary and secondary diversion ratios, significantly influence capacity. Notably, the capacity exhibits a marked quadratic polynomial relationship with the primary diversion ratio and diversion point spacing, and a linear relationship with the secondary diversion ratio. The model's validity is confirmed through a case study at the diversion area north of Huacun Interchange in Chongqing Municipality, where the discrepancy between calculated and actual capacities is under 5%, underscoring the model's high accuracy. These results offer valuable theoretical and methodological support for the planning, design, and traffic management of diversion areas.

Design and Testing of Key Components for a Multi-Stage Crushing Device for High-Moisture Corn Ears Based on the Discrete Element Method

To improve the crushing efficiency and crushing pass rate of high-moisture corn ears (HMCEs), a multi-stage crushing scheme is proposed in this paper. A two-stage crushing device for HMCEs is designed, and the ear crushing process is analyzed. Firstly, a simulation model for HMCEs was established in EDEM software (2018), and the accuracy of the model was verified by the shear test. Subsequently, single-factor simulation experiments were conducted, with the crushing rate serving as the evaluation index. The optimal working parameter ranges for the HMCE device were identified as a primary crushing roller speed of 1200–1600 revolutions per minute (r/min), a secondary crushing roller clearance of 1.5–2.5 mm, and a secondary crushing roller speed of 2750–3750 r/min. A Box–Behnken experiment was conducted to establish a multiple regression equation. With the objective of maximizing the qualified crushing pass rate, the optimal combination of parameters was revealed: a primary crushing roller speed of 1500 r/min, a secondary crushing roller clearance of 2.5 mm, and a secondary crushing roller speed of 3280 r/min. The pass rate of corn cob crushing in the simulation test was 98.2%. The physical tests, using the optimized parameter combination, yielded a qualified crushing rate of 97.5%, which deviates by 0.7% from the simulation results, satisfying the requirement of a qualified crushing rate exceeding 95%. The experimental outcomes validate the rationality of the proposed crushing scheme and the accuracy of the model, providing a theoretical foundation for subsequent research endeavors.

A guided twin delayed deep deterministic reinforcement learning for vaccine allocation in human contact networks

This manuscript introduces an innovative approach to optimizing the distribution of a limited vaccine resource within a population modeled as a contact network, aiming to mitigate the spread of infectious diseases. The study develops a novel methodology that combines reinforcement learning and graph neural networks. To understand the dynamics of disease propagation, the study constructs an analytical model that outlines conditions for disease eradication or endemic states. This model supports a series of simulation experiments across various scenarios, demonstrating the proposed method’s superiority over random and centrality-based approaches in reducing the average number of infections per individual during an outbreak. The adaptability of the proposed method is further emphasized by its robust performance across networks of diverse sizes and configurations, highlighting its real-world applicability. The findings of this study have significant implications for public health policy and resource allocation, offering a promising framework for managing infectious disease outbreaks in complex and dynamic environments.

A hybrid particle swarm optimisation for flexible casting job shop scheduling problem with batch processing machine

AbstractA flexible casting job shop scheduling problem (FCJSP) with batch processing machines is proposed based on the analysis of the flexible job shop scheduling problem (FJSP) and the study of the expendable casting process. Considering the makespan under the influence of the energy consumption, the authors apply the time execution window to the FCJSP model in conjunction with the characteristics of casting production. A hybrid particle swarm optimisation algorithm (HPSO) is developed to solve the FCJSP. The HPSO employs a block integration decoding rule to address scheduling integration. Particle swarm optimisation is used for global search, employing both discrete and continuous search strategies. Furthermore, the local search employs tabu search with neighbourhood operations based on knowledge‐driven techniques. Simulation experiments demonstrate the feasibility of the proposed optimisation model. In the end, the HPSO algorithm has been successfully applied to the real expendable casting scheduling. The results demonstrate that it is more efficient and robust than previously reported algorithms.

Typical Tire Additives in River Water: Leaching, Transformation, and Environmental Risk Assessment.

Tire wear particles (TWPs) released during vehicle driving can enter water bodies, leading to leaching of tire additives (TAs) in aquatic environments. However, the transformation behavior and related ecological impacts of TAs and their transformation products (TPs) remain unclear. In this study, laboratory-based simulation experiments and field investigations were conducted to explore the transformation mechanisms and ecological risks of TAs. After being placed in river water for 24 h, about 7-95% of 12 investigated TAs in TWPs were leached. Forty-eight TPs from eight TAs were tentatively identified along with different transformation pathways via suspect screening by high-resolution mass spectrometry. Semiquantitative results indicated that TPs derived from N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylene-diamine (6PPD) were predominant in leachates, while aryl hydrolysis and quinone pathways were the main transformation pathways. Field investigations on urban surface water samples from 16 sites in Hong Kong revealed the occurrence of 17 TAs and 1 TP, with concentrations ranging from 13.9 to 2230 ng/L (median ± standard deviation: 226 ± 534 ng/L). Sixteen TPs from six TAs were additionally identified via suspect screening. It is estimated that 6PPD-quinone and seven TAs could pose medium to high ecological risk, while N-(1,3-dimethylbutyl)-N'-phenyl-p-quinonediimine, a frequently detected TP, was identified as a persistent-bioaccumulative-toxic substance.

Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations

Abstract The article establishes a depositional model for lacustrine hyperpycnal flow by examining dynamics, transport factors, and laminae formation. The results show that several typical experimental phenomena such as fluid front mixing, double flow division, underwater leap, water skiing, and “new head” can be observed in the flume experiment. Based on the experimental observation of the flow process, three modes of transport of hyperpycnal flow in freshwater lake basins are summarized: bottom-bed loading, suspended loading, and uplift loading. Further, the change of fluid properties in hyperpycnal flow is summarized in three stages: a high-concentration stage, a low-concentration stage, and an uplifting stage. There are two main factors affecting the long-range transport of hyperpycnal flow: (1) the concentration difference between the head deposits and the ambient water body and (2) shear force of turbulence in the upper part of hyperpycnal flow. The simulation experiments of hyperpycnites laminae show that the laminae change from continuous to intermittent with the increase of the transportation distance. It is clear that the mode of transport of the hyperpycnal flow has a controlling effect on the degree of development of the laminae. Eventually, a depositional model of lake-facies hyperpycnal flow under experimental conditions was constructed.

A Model-Free Adaptive Positioning Control Method for Underactuated Unmanned Surface Vessels in Unknown Ocean Currents

Aiming to address the problem of underactuated unmanned surface vehicles (USVs) performing fixed-point operations at sea without dynamic positioning control systems, this paper introduces an original approach to positioning control: the virtual anchor control method. This method is applicable in environments with currents that change slowly and does not require prior knowledge of current information or vessel motion model parameters, thus offering convenient usability. This method comprises four steps. First, a concise linear motion model with unknown disturbances is proposed. Then, a motion planning law is designed by imitating underlying principles of ship anchoring. Next, an adaptive disturbance observer is proposed to estimate uncertainties in the motion model. In the last step, based on the observer, a sliding-mode method is used to design a heading control law, and a thrust control law is also designed by applying the Lyapunov method. Numerical simulation experiments with significant disturbances and tidal current variations are conducted, which demonstrate that the proposed method has a good control effect and is robust.

Modeling and Calculation of Limit Magnitude Detection of Orbital Optoelectric Tracking System

In order to evaluate the tracking capability of optoelectric tracking for an orbital target, the limit magnitude detection performance calculation model and its calculation method are studied. Combining the optical signal characteristics of the tracked orbital target, the background, and the CCD noise, the framework of the limit magnitude calculation model of the system for dynamic target detection is constructed. The relationships between the limit magnitude and the signal-to-noise ratio threshold of the optical signal characteristics, the exposure time of the CCD camera, and the dark current of the CCD imaging are studied and analyzed while considering the sunlight illumination condition, so that the calculation function and its change curve are given. The limit magnitude detection capability of the system is verified by the simulated experiment and the synchronized tracking test, and the detection distance maximum error of the model calculation is 3.6 m. The results show that under certain illumination conditions, when the exposure time of the CCD camera is longer and the SNR threshold is lower, the limit magnitude detection performance of the system is better, and the tracking performance of the system is more stable.

Three-Stage Cascade Information Attenuation for Opinion Dynamics in Social Networks.

In social network analysis, entropy quantifies the uncertainty or diversity of opinions, reflecting the complexity of opinion dynamics. To enhance the understanding of how opinions evolve, this study introduces a novel approach to modeling opinion dynamics in social networks by incorporating three-stage cascade information attenuation. Traditional models have often neglected the influence of second- and third-order neighbors and the attenuation of information as it propagates through a network. To correct this oversight, we redefine the interaction weights between individuals, taking into account the distance of opining, bounded confidence, and information attenuation. We propose two models of opinion dynamics using a three-stage cascade mechanism for information transmission, designed for environments with either a single or two subgroups of opinion leaders. These models capture the shifts in opinion distribution and entropy as information propagates and attenuates through the network. Through simulation experiments, we examine the ingredients influencing opinion dynamics. The results demonstrate that an increased presence of opinion leaders, coupled with a higher level of trust from their followers, significantly amplifies their influence. Furthermore, comparative experiments highlight the advantages of our proposed models, including rapid convergence, effective leadership influence, and robustness across different network structures.

Research on defect image inversion method based on multiple lift-off values magnetic memory signals

Metal magnetic memory (MMM) technique can detect macroscopic defects and stress concentration zones of ferromagnets, and it can identify the approximate positions of these flaws, while a little further information about defects characteristics can be provided. To promote study in this area, defects were modeled as located magnetic dipoles whose strength and locations should be determined, and the technique of truncated generalized inverse and reduced space inversion were used to image the dipole strength. Through the simulation experiment, we found that the inversion image quality was influenced by the lift-off value. When the magnetic field data for inversion was measured with a small lift-off value, the inner dipoles contributions were easily buried and the inversion image can only shown the surface dipole well. In contrast, if the magnetic field data was measured with a greater lift-off value, it was possible to inverse the deep dipoles while the image was somewhat out of focus. To overcome this limitation, we composed the magnetic field data measured under different lift-off values together for inversion. We used the same model to test this approach and applied it on real crack defects magnetic field data. It demonstrates that the new inversion approach shows better accuracy than the single lift-off value and it was possible to infer the location and size of defect by the evaluation of preset dipoles.

Multi-UAV Cooperative Pursuit of a Fast-Moving Target UAV Based on the GM-TD3 Algorithm

Recently, developing multi-UAVs to cooperatively pursue a fast-moving target has become a research hotspot in the current world. Although deep reinforcement learning (DRL) has made a lot of achievements in the UAV pursuit game, there are still some problems such as high-dimensional parameter space, the ease of falling into local optimization, the long training time, and the low task success rate. To solve the above-mentioned issues, we propose an improved twin delayed deep deterministic policy gradient algorithm combining the genetic algorithm and maximum mean discrepancy method (GM-TD3) for multi-UAV cooperative pursuit of high-speed targets. Firstly, this paper combines GA-based evolutionary strategies with TD3 to generate action networks. Then, in order to avoid local optimization in the algorithm training process, the maximum mean difference (MMD) method is used to increase the diversity of the policy population in the updating process of the population parameters. Finally, by setting the sensitivity weights of the genetic memory buffer of UAV individuals, the mutation operator is improved to enhance the stability of the algorithm. In addition, this paper designs a hybrid reward function to accelerate the convergence speed of training. Through simulation experiments, we have verified that the training efficiency of the improved algorithm has been greatly improved, which can achieve faster convergence; the successful rate of the task has reached 95%, and further validated UAVs can better cooperate to complete the pursuit game task.

Chaotic laser time series prediction based on an improved logistic mapping algorithm echo state network

Chaos synchronization plays vital functions in the fields of optical chaos secure communication. The synchronization performance can be significantly degraded by parameter mismatches between the chaotic transmitter and receiver. In this paper, the Deep-Logistical Mapping Echo State Network (D-LMESN) is proposed to enhance the performance of chaos synchronization. The network is upgraded by using an improved logical mapping algorithm and a deep reserve pool structure with phase space reconstruction. Results show that D-LMESN exhibits better performance in the prediction of chaotic time series, thanks to the adaptive parameter adjustment, which increases the ability to capture the dynamic characteristics of complex systems. Compared with ESN, the mean square error of this model is reduced by 55% and 72%, respectively, in chaotic laser simulation and actual data experiments. This provides a new possibility, to our knowledge, for the development of chaotic secure communication.

Performance Evaluation of a Bioinspired Geomagnetic Sensor and Its Application for Geomagnetic Navigation in Simulated Environment.

For geomagnetic navigation technology, taking inspiration from nature and leveraging the principle of animals' utilization of the geomagnetic field for long-distance navigation, and employing biomimetic technology to develop higher-precision geomagnetic sensors and more advanced navigation strategies, has emerged as a new trend. Based on the two widely acknowledged biological magnetic induction mechanisms, we have designed a bioinspired weak magnetic vector (BWMV) sensor and integrated it with neural networks to achieve geomagnetic matching navigation. In this paper, we assess the performance of the BWMV sensor through finite element model simulation. The result validates its high measurement accuracy and outstanding adaptability to installation errors with the assistance of specially trained neural networks. Furthermore, we have enhanced the bioinspired geomagnetic navigation algorithm and proposed a more advanced search strategy to adapt to navigation under the condition of no prior geomagnetic map. A simulated geomagnetic navigation platform was constructed based on the finite element model to simulate the navigation of the BWMV sensor in geomagnetic environments. The simulated navigation experiment verified that the proposed search strategy applied to the BWMV sensor can achieve high-precision navigation. This study proposes a novel approach for the research of bioinspired geomagnetic navigation technology, which holds great development prospects.

A pre-action indication of making stop/go decisions at flashing-light-controlled grade crossings based on drivers’ EEG information: A driving simulation experiment with audio-warning assistance

This study found that the electroencephalogram (EEG) information is a prior indication for sensing drivers’ imminent actions. It is possible to get to know the drivers’ intentions before their next moves, providing huge potentials of early interventions on the occurrence of accidents. A driving simulation experiment was conducted to identify EEG patterns during pre-action decision-making, assess their roles in stop/go decisions, and evaluate the impact of warnings on EEG signals. Four driving groups were divided based on drivers’ stop/go decisions and the influence of dilemma-zone effect, which could reflect drivers’ abilities to stop safely at the onset of flashing red light. The results revealed significant variations in EEG signals among the four driving groups, highlighting EEG as a sensitive pre-action indication. EEG changes indicated that with warning assistance, drivers have enhanced situational awareness, are able to make a stop/go decision in advance, and have less workload on visual search when approaching grade crossings. Such EEG patterns could interpret drivers’ stop/go decisions with a highest accuracy of 97.11%, and could identify potentially dangerous behaviors. These insights have implications for pre-event intervention based on EEG monitoring using non-contact devices.

Mitigating Intermittent Connectivity Problems in Vehicle-to-Vehicle Communication (V2VC): A Sparse Network Computational Model (SNCM)

INTRODUCTION: Wireless communication has made remarkable progress, by the rapid development of wireless technology in Artificial Intelligence (AI). Intelligent Transportation Systems (ITS), and Vehicular Ad Hoc Networks (VANETs) have received significant attention to ensure safety. However, V2V communication in VANETs faces uncontrollable challenges due to frequent intermittent connectivity issues in infrastructure-less networks. Addressing these problems in both safety and non-safety applications is a complex task.OBJECTIVES: To mitigate the intermittent connectivity problems, a novel Sparse Network Computational Model (SNCM) was proposed.METHODS: Extensive simulations using MATLAB to analyze the impact of spatial-temporal variations under different traffic flow densities. We varied the sensitivity factor (λ) at different time intervals while maintaining a constant traffic density. RESULTS: The findings indicate that there is no need to increase λ beyond certain thresholds for each level of service. The simulation results provide valuable guidelines for designing sparse networks, effectively mitigating frequent intermittent disconnections. Simulation experiments revealed an optimal threshold for the sensitivity factor λ for each level of service. Increasing λ beyond certain thresholds did not yield significant improvements in mitigating disconnections in V2V communication.CONCLUSION: The results provide valuable insights and guidelines for designing sparse networks to enhance connectivity and address intermittent disconnection issues. This paper presents a groundbreaking endeavor, and therefore, direct comparisons with existing protocols to evaluate its overall performance are beyond the scope of this paper. Instead, the SNCM protocol is intended to set a standard for future researchers to benchmark their research contributions against.

Effect of Microplastics and Straw Addition on Nitrogen and Phosphorus Leaching in Orange Orchard Soils

To explore the effects of microplastic input and straw addition on nitrogen and phosphorus leaching in orange orchard soil, indoor soil column leaching simulation experiments were conducted on orange orchard soil in Dangyang City, Hubei Province. The experiment analyzed the effects of different treatments on the leaching characteristics of soil nitrogen and phosphorus. The results showed that： ① The increase in microplastic input increased the leaching capacity of the soil （TN, NO3--N, NH4+-N, and TP） when only microplastic input was added. ② Under the addition of straw, the input of microplastics increased, which reduced the leaching amount of TN and NO3--N in the soil, and increased the leaching amount of NH4+-N and TP in the soil. ③ Under the input of microplastics, the key influencing factors of soil nitrogen and phosphorus leaching were soil bulk density and water content. Under straw addition, it was mainly affected by soil carbon and nitrogen content. Among them, microplastic input and straw addition significantly increased soil pH, but their path coefficients for nitrogen and phosphorus leaching were not significantly correlated. The results showed that the effects of polypropylene microplastic input and straw addition on soil nitrogen and phosphorus leaching were related to the amount of microplastics input and whether straw was added. The results showed that the input of microplastics would increase the amount of soil nitrogen and phosphorus leaching. Although nitrogen leaching loss caused by microplastic input could be reduced under straw addition, soil phosphorus leaching loss was significantly increased.

Edge Caching Placement Strategy based on Evolutionary Game for Conversational Information Seeking in Edge Cloud Computing

In Internet applications, network conversation is the primary communication between the user and server. The server needs to efficiently and quickly return the corresponding service according to the conversation sent by the user to improve the users’ Quality of Service. Thus, Conversation Information Seeking (CIS) research has become a hot topic today. In Cloud Computing (CC), a central service mode, the conversation is transmitted between the user and the remote cloud over a long distance. With the explosive growth of Internet applications, network congestion, long-distance communication, and single point of failure have brought new challenges to the centralized service mode. People put forward Edge Cloud Computing (ECC) to meet the new challenges of the centralized service mode of CC. As a distributed service mode, ECC is an extension of CC. By migrating services from the remote cloud to the network edge closer to users, ECC can solve the above challenges in CC well. In ECC, people solve the problem of CIS through edge caching. The current research focuses on designing the edge cache strategy to achieve more predictable caching. In this article, we propose an edge cache placement method Evolutionary Game based Caching Placement Strategy (EG-CPS). This method consists of three modules: the user preference prediction module, the content popularity calculation module, and the cache placement decision module. To maximize the predictability of the cache strategy, we are committed to optimizing the cache hit rate and service latency. The simulation experiment compares the proposed strategy with several other cache strategies. The experimental results illustrate that EG-CPS can reduce up to 2.4% of the original average content request latency, increase the average direct cache hit rate by 1.7%, and increase the average edge cache hit rate by 3.3%.